Unlike most previous studies that use machine learning in the space of building damage and climate change [1]}, [2]}, [3]}, which focus on after-the-fact damage estimation of a natural hazard occurrence, we introduce PreDisM, which models susceptibility of buildings, landscapes, and habitats for a future calamity. Forecasting the future risks allows residents and governments to take stock of situations with an appropriate contingency fund, facilities, and preparedness. PreDisM doesn't depend on disaster occurrence, rather it predicts damages that could happen from a future hazard with a given intensity. This is highly beneficial to affected communities if the future hazard risk, given the climate risk, could be identified without the massive past data but with the current community's information (e.g., present satellite imagery over the area). For instance, identifying building damage levels prior to a hazard of an arbitrary magnitude hitting the area would provide an insight to the stakeholders and communities. Our models are fine-tuned on disaster-type open-source building datasets [4]}, along with adjoining unifying fully connected layers activated with meta-information to generate building-level hazard-level risks of future damage.
